import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# from sklearn.model_selection import train_test_split
from sqlalchemy import create_engine
from matplotlib import pyplot as plt
import statsmodels as sm
import seaborn as sns 
import pymysql

db_config = {
    'host': '127.0.0.1',
    'user': 'root',
    'password': 'root',
    'database': 'sjwj',
    'port':3306,
    'charset': 'utf8'
}

engine = create_engine(
    f"mysql+pymysql://{db_config['user']}:{db_config['password']}@{db_config['host']}:{db_config['port']}/{db_config['database']}?charset={db_config['charset']}"
)

conn = pymysql.connect(**db_config)

chunk_size = 10000

df = pd.read_sql_query(
    """
    SELECT d.*, m.buy_lg_vol, m.sell_lg_vol, m.buy_elg_vol, m.sell_elg_vol, m.net_mf_vol, i.vol as i_vol, i.closes as i_closes FROM date_1 d JOIN moneyflows m on d.ts_code = m.ts_code and d.trade_date = m.trade_date
    LEFT JOIN index_daily i on d.trade_date = i.trade_date and i.ts_code = '399001.SZ'
    WHERE d.trade_date BETWEEN '2023-01-01' and '2024-01-01' and d.ts_code = '002229.SZ'
    """,
    conn,
    chunksize=chunk_size
)

df1 = pd.concat(df, ignore_index=True)

df1['zd_closes'] = round((df1['closes'] - df1['closes'].shift(1))/ df1['closes'].shift(1), 2)
df1['zs_closes'] = round((df1['i_closes'] - df1['i_closes'].shift(1))/ df1['i_closes'].shift(1), 2)
df1['zs_vol'] = round((df1['i_vol'] - df1['i_vol'].shift(1))/ df1['i_vol'].shift(1), 2)

# print(df1.head)


#处理缺失值
df1 = df1.dropna(subset=['zd_closes', 'zs_closes', 'zs_vol'])
ex = ['id', 'ts_code', 'trade_data', 'the_data', 'opens', 'high', 'low', 'closes', 'pre_closes', 'changes', 'pct_chg', 'amount', 'vol', 'zs_closes', 'zs_vol', 'buy_lg_vol', 'sell_lg_vol', 'i_closes','i_vol']
number = df1.select_dtypes(include = ['number']).columns.to_list()
newList = [col for col in number if col not in ex]

formuls = 'zd_closes ~' + ' + '.join(newList)
res = smf.ols(formuls, data=df1).fit()  
print(res.summary())

#可视化查看
plt.figure(figsize=(10,6))
plt.scatter(res.fittedvalues,df1['zd_closes'])
plt.show()

features = df1[['buy_elg_vol', 'sell_elg_vol', 'net_mf_vol']]
correlation_matrix = features.corr()

plt.figure(figsize=(10,6))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm',  cbar=True, fmt='.2f', linewidths=.5)
plt.show()